In this role the successful candidate will work alongside a talented group of engineers and scientists supporting the industrialisation of Li-ion batteries and cells. Reporting to the Head of Product Engineering this position will support the capture and analysis of data generated from the production and test of UKBIC baseline and customer products.
The purpose of this role is to deliver data insights that assess the impact of manufacturing variation on product performance, thereby determining product and process improvement opportunities to enable successful scale-up to quality.
The company has recently deployed a Manufacturing Execution System (MES) to capture equipment, process and quality data across its facilities and is developing the associated data pipelines into Siemens Insights Hub. This role will act as the Product Engineering owner and internal customer for analytics capability development, working closely with the Data Engineer and Manufacturing Systems Integration Engineer to define data structures, traceability requirements, dashboards and analytical workflows. The successful candidate will help shape the evolution of Insights Hub from foundational time-series data visualisation and reporting through to advanced statistical analysis, modelling, machine learning and predictive analytics capabilities.
The analytics capability is expected to evolve in stages, beginning with establishing access to manufacturing and test data through time-series visualisation and traceability, before advancing into variable-to-variable correlations, process characterisation, feature engineering, statistical modelling, predictive analytics and machine learning. The successful candidate will play a leading role in defining and delivering this capability roadmap.
In summary, the key deliverables for this role are:
1. Define and deliver the Product Engineering analytics roadmap for Siemens Insights Hub.
2. Deliver and report data insights to customer and internal programmes, on time and to target.
3. Develop and continuously improve analytical methods, models and tools that support data-driven decision making.
4. Capture, document and communicate analytical methods, models and findings to build organisational capability.
5. Develop code to support the above activity and implement processes that ensure these are stored securely and version controlled.
Key Accountabilities and Responsibilities
- Act as the Product Engineering owner and internal customer for Siemens Insights Hub analytics capability development.
- Define data, traceability, reporting and dashboard requirements in collaboration with Data Engineering and Manufacturing Systems Integration teams.
- Promote a culture of data-driven decision making through effective analysis, visualisation and communication of insights.
- Determine relationships between manufacturing process parameters, quality measurements, defects and product performance.
- Analyse manufacturing and test data to identify trends, root causes and opportunities for product, process, yield and quality improvement.
- Develop derived metrics, calculated features and engineering insights from raw manufacturing and test data.
- Apply statistical methods, data modelling and machine learning techniques to improve process understanding and predict product outcomes.
- Create and programme data models and analytical tools using simulated and real-world datasets to support engineering and business decisions.
- Provide analytical inputs to other engineering capabilities, including product and process simulation activities.
- Implement and execute good practice in the secure storage and control of developed code.
- Make data-driven recommendations that facilitate the continuous improvement of manufacturing processes and product performance
Key Interactions
- As well as working directly with Product Engineering peers the Data scientists will work cross functionally within the project team including:
- Manufacturing Engineering & Quality – Capturing and post processing of on-line of off-line measurement data. Determine data dashboard requirement for Siemens Insights Hub.
- Digital Manufacturing – Working closely with the Data Engineer and Manufacturing Systems Integration Engineer as the Product Engineering customer for Siemens Insights Hub. Define data, traceability and analytics requirements, validate delivered capabilities and help shape the roadmap from basic reporting through to advanced analytics and predictive modelling
- Product engineering – Analysing product performance, quality and validation data to identify trends, establish relationships with manufacturing processes and support product and process improvement activities.
- Simulation Engineers – providing real data to support simulation model validation, develop reduced order data models from large, simulated data sets. Work with data engineering and simulation to package models into software for internal and external customers.
- All functions – presenting findings and insights.
Required Qualifications, Skills & Experience:
- Master Degree qualified MEng/MSc at 2:1 or higher in a relevant Engineering or Science discipline (such as but not limited to Computer Science, Mathematics, Software Engineering, Physics).
- Relevant industrial experience extracting insights from high-volume manufacturing, process engineering, operational or quality datasets, including analysis of continuous time-series data.
- Excellent analytical and code programming skills (preferably Python and Java).
- Experience in SQL databases and cloud-based data platforms.
- Experience using data visualisation and dashboarding tools.
- Experience defining analytics requirements and communicating effectively with software, data engineering or digital systems teams.
- Strong understanding of statistical methods, data modelling and machine learning, and their application to manufacturing, process or product engineering challenges.
- Experience developing predictive models, analytical workflows or machine learning solutions using industrial datasets.
- Excellent knowledge of Microsoft tools (Excel, Visual Basic, Access, Word, PowerPoint).
- Basic understanding of lithium-ion cell chemistry, cell manufacturing processes and battery performance characterisation.
- Experience of manufacturing traceability, genealogy and process capability analysis.
- Exposure to Six Sigma, Design of Experiments (DOE), Statistical Process Control (SPC) or robust engineering methods.
- Experience using Siemens Insights Hub, Siemens Xcelerator, Databricks, Power BI or equivalent industrial analytics platforms.
- Experience applying machine learning or AI techniques within manufacturing or industrial environments.
Personal attributes:
- Be customer focused and results driven.
- Be an excellent communicator and able to adapt to various audiences and levels.
- Have a solution-focused mindset when faced with challenges or ambiguity.
- Thrive to learn from and collaborate with others.
- Diligent but flexible in approach, particularly with imperfect data.
- Work proactively to gather data and naturally inquisitive.
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